Synergistic Integration Between Machine Learning and Agent-Based Modeling: A Multidisciplinary Review

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 5; pp. 2170 - 2190
• Main Authors: Zhang, Wei, Valencia, Andrea, Chang, Ni-Bin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Agent-based modeling (ABM); Agent-based models; Algorithms; behavioral intervention; Biological system modeling; Changing environments; Cognition; Data acquisition; Decision making; Environmental changes; Learning algorithms; Machine learning; machine learning (ML); Modelling; Neutrons; Numerical models; Predictive models; reinforcement learning (RL); Situational awareness
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3106777

Agent-based modeling (ABM) involves developing models in which agents make adaptive decisions in a changing environment. Machine-learning (ML) based inference models can improve sequential decision-making by learning agents' behavioral patterns. With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents' behavioral rules into an adaptive model. Even though there are plenty of studies that apply ML in ABMs, the generalized applicable scenarios, frameworks, and procedures for implementations are not well addressed. In this article, we provide a comprehensive review of applying ML in ABM based on four major scenarios, i.e., microagent-level situational awareness learning, microagent-level behavior intervention, macro-ABM-level emulator, and sequential decision-making. For these four scenarios, the related algorithms, frameworks, procedures of implementations, and multidisciplinary applications are thoroughly investigated. We also discuss how ML can improve prediction in ABMs by trading off the variance and bias and how ML can improve the sequential decision-making of microagent and macrolevel policymakers via a mechanism of reinforced behavioral intervention. At the end of this article, future perspectives of applying ML in ABMs are discussed with respect to data acquisition and quality issues, the possible solution of solving the convergence problem of reinforcement learning, interpretable ML applications, and bounded rationality of ABM.